Li Zebin, Gao Lifu, Zhang Gang, Lu Wei, Wang Daqing, Zhang Jinzhong, Cao Huibin
Anhui Undergrowth Crop Intelligent Equipment Engineering Research Center, West Anhui University, Lu'an 237012, China.
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.
Bioengineering (Basel). 2024 May 9;11(5):470. doi: 10.3390/bioengineering11050470.
Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.
肌动图（MMG）是一种重要的肌肉生理活动信号，能够反映所募集运动单位的数量以及收缩频率。因此，MMG可用于估计骨骼肌产生的力量。然而，串扰和时间序列相关性在现实世界中严重影响MMG信号识别。这些因素限制了动态肌肉力量估计的准确性及其在可穿戴设备中的交互能力。为了解决这些问题，首先提出一个假设，即通过使用来自串扰较少的单一肌肉的MMG信号可以提高膝关节动态伸展力估计的准确性。然后使用不同肌肉信号特征组合的估计结果对该假设进行验证。最后，提出了一种新型模型（改进灰狼优化器优化的长短期记忆网络，即IGWO-LSTM），以进一步提高膝关节动态伸展力估计的性能。实验结果表明，来自串扰较少的单一肌肉的MMG信号具有卓越的估计膝关节动态伸展力的能力。此外，与其他现有先进模型相比，所提出的IGWO-LSTM具有最佳的性能指标。我们的研究不仅有望增进对股四头肌收缩机制的理解，还能提高未来康复和辅助设备的灵活性及交互能力
